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首页> 外文期刊>International Journal of Hybrid Intelligent Systems >Using the concept of instance typicality instance-based learning environments involving nominal attributes
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Using the concept of instance typicality instance-based learning environments involving nominal attributes

机译：使用实例概念涉及标称属性的基于基于基于实例的学习环境

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摘要

Instance-Based Learning (IBL) is a machine learning research area with focus on supervised algorithms that use the given training set as the expression of the learned concept. Usually the training instances in the set are described by vectors of attribute values and an associated class. The generalization process conducted by an instance-based algorithm happens during the classification phase, when a class should be assigned to a new instance of unknown class. Attributes that describe instances can be of different types, depending on the values they represent and, usually, can be of discrete or continuous type. A subtype of the discrete type is known as nominal. An attribute of nominal type usually represents categories and there is no order among its possible values. This paper proposes and investigates an alternative strategy for dealing with nominal attributes during the classification phase of the well-known instance-based algorithm NN (Nearest Neighbor). The proposed strategy is based on the concept of typicality of an instance, which can be taken into account as a possible tiebreaker, in situations where the new instance to be classified is equidistant from more than one nearest neighbor. Experiments using the proposed strategy and the default random strategy used by the conventional NN show that a strategy based on the concept of instance typicality can be a convenient choice to improve accuracy, when data instances have nominal attributes among the attributes that describe them.
机译：基于实例的学习（IBL）是一台机器学习研究区域，专注于使用给定培训集作为学习概念的表达的监督算法。通常，该组中的培训实例由属性值和关联类的vector描述。在分类阶段期间，当应将类分配给一个新的未知类实例时，通过基于实例的算法进行的泛化过程。描述实例的属性可以是不同类型的，具体取决于它们所代表的值，通常，可以是离散或连续类型。离散类型的亚型称为标称值。名义类型的属性通常表示类别，并且可能的值中没有订单。本文提出并调查了在众所周知的基于实例的算法NN（最近邻居）的分类阶段期间处理名义属性的替代策略。拟议的策略基于一个实例的典型特性，可以考虑到可能的纠正仪，在待分类的新实例的情况下与一个以上的最近邻居等距离。使用所提出的策略和传统NN使用的默认随机策略的实验表明，当数据实例在描述它们的属性中具有标称属性时，基于实例典型程度的概念的策略可以是一种方便的选择，以提高准确性。

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